In many data mining and machine learning applications, data are not free, and there is a test cost for each data item. Due to economic, technological and legal reasons, it is neither possible nor necessary to obtain a classifier with 100% accuracy. In this paper, we consider such a situation and propose a new constraint satisfaction problem to address it. With this in mind, one has to minimize ...

In this paper, the attributes employed to model the constraints are called constraint attributes and those attributes involved in the objective function to be optimized are called cost-optimal attributes. The constrained clustering considered is conducted in such a way that the objective function of cost-optimal attributes is optimized subject to the condition that the imposed constraint is sat...

This paper addresses the problem of how a robot can autonomously improve a manipulation skill in an efficient and secure manner. Instead of using the standard reinforcement learning formulation where all objectives are defined in a single reward function, we propose a generalized formulation that consists of three components: 1) A known analytic cost function; 2) A black-box reward function; 3)...

Many combinatorial problems encountered in practice involve constraints that require that a set of variables take distinct or equal values. The AllDifferent constraint, in particular, ensures that all variables take distinct values. Two soft variants of this constraint were proposed in [4], defined either with respect to a so-called variable or graph-based cost function. When requiring similari...

‎in this paper, using the idea of convexificators, we study boundedness and nonemptiness of lagrange multipliers satisfying the first order necessary conditions. we consider a class of nons- mooth fractional programming problems with equality, inequality constraints and an arbitrary set constraint. within this context, define generalized mangasarian-fromovitz constraint qualification and show t...

The development of a system based on constraint programming includes two main phases: first, the problem to be solved is formulated as a constraint satisfaction problem; then, the formulation is implemented in a constraint-programming language. Constraint-programming research has mainly concentrated on the second phase, by studying powerful declarative languages that automatically propagate the...

This paper presents a Genetic Algorithm (GA) approach for solving constrained reliability optimization problem of the five unit bridge network. Considering uncertainty for cost, the reliability optimization problem has been solved by GA technique to maximize system reliability. GA is an efficient method for solving this type of optimization problems. This paper successfully applies the GA techn...

The paper aims to present an application of constraint programming techniques for project portfolio scheduling taking into account the imprecision in activity duration and cost. Data specification in the form of discrete α-cuts allows combining distinct and imprecise data, and implementing a constraint satisfaction problem with the use of constraint programming. Moreover using α-cuts, optimisti...

Owing to rising intentional events, supply chain disruptions have been considered by setting up a game between two players, namely, a designer and an interdictor contesting on minimizing and maximizing total cost, respectively. The previous studies have found the equilibrium solution by taking transportation, penalty and restoration cost into account. To contribute further, we examine how incor...